OpenRaiser/Intern-Atlas huggingface dataset:
A Hierarchical Decomposition of Kullback-Leibler Divergence: Disentangling Marginal Mismatches from Statistical Dependencies
Venue: arXiv cs.OH 2025
Year: 2025

The Kullback-Leibler (KL) divergence is a foundational measure for comparing probability distributions. Yet in multivariate settings, its single value often obscures the underlying reasons for divergence, conflating mismatches in individual variable distributions (marginals) with effects arising from statistical dependencies. We derive an algebraically exact, additive, and hierarchical decomposition of the KL divergence between a joint distribution P(X1,...,Xn) and a standard product reference distribution Q(X1,...,Xn) = product_i q(Xi), where variables are assumed independent and identically distributed according to a common reference q. The total divergence precisely splits into two primary components: (1) the summed divergence of each marginal distribution Pi(Xi) from the common reference q(Xi), quantifying marginal deviations; and (2) the total correlation (or multi-information), capturing the total statistical dependency among variables. Leveraging Mobius inversion on the subset lattice, we further decompose this total correlation term into a hierarchy of signed contributions from distinct pairwise, triplet, and higher-order statistical interactions, expressed using standard Shannon information quantities. This decomposition provides an algebraically complete and interpretable breakdown of KL divergence using established information measures, requiring no approximations or model assumptions. Numerical validation using hypergeometric sampling confirms the decomposition's exactness to machine precision across diverse system configurations.
Towards Stepwise Domain Knowledge-Driven Reasoning Optimization and Reflection Improvement
Venue: arXiv cs.AI 2025
Year: 2025

Recently, stepwise supervision on Chain of Thoughts (CoTs) presents an enhancement on the logical reasoning tasks such as coding and math, with the help of Monte Carlo Tree Search (MCTS). However, its contribution to tasks requiring domain-specific expertise and knowledge remains unexplored. Motivated by the interest, we identify several potential challenges of vanilla MCTS within this context, and propose the framework of Stepwise Domain Knowledge-Driven Reasoning Optimization, employing the MCTS algorithm to develop step-level supervision for problems that require essential comprehension, reasoning, and specialized knowledge. Additionally, we also introduce the Preference Optimization towards Reflection Paths, which iteratively learns self-reflection on the reasoning thoughts from better perspectives. We have conducted extensive experiments to evaluate the advantage of the methodologies. Empirical results demonstrate the effectiveness on various legal-domain problems. We also report a diverse set of valuable findings, hoping to encourage the enthusiasm to the research of domain-specific LLMs and MCTS.
A Practical Approach to using Supervised Machine Learning Models to Classify Aviation Safety Occurrences
Venue: arXiv cs.LG 2025
Year: 2025

This paper describes a practical approach of using supervised machine learning (ML) models to assist safety investigators to classify aviation occurrences into either incident or serious incident categories. Our implementation currently deployed as a ML web application is trained on a labelled dataset derived from publicly available aviation investigation reports. A selection of five supervised learning models (Support Vector Machine, Logistic Regression, Random Forest Classifier, XGBoost and K-Nearest Neighbors) were evaluated. This paper showed the best performing ML algorithm was the Random Forest Classifier with accuracy = 0.77, F1 Score = 0.78 and MCC = 0.51 (average of 100 sample runs). The study had also explored the effect of applying Synthetic Minority Over-sampling Technique (SMOTE) to the imbalanced dataset, and the overall observation ranged from no significant effect to substantial degradation in performance for some of the models after the SMOTE adjustment.
PQS (Prune, Quantize, and Sort): Low-Bitwidth Accumulation of Dot Products in Neural Network Computations
Venue: arXiv cs.LG 2025
Year: 2025

We present PQS, which uses three techniques together - Prune, Quantize, and Sort - to achieve low-bitwidth accumulation of dot products in neural network computations. In conventional quantized (e.g., 8-bit) dot products, partial results are accumulated into wide (e.g., 32-bit) accumulators to avoid overflows when accumulating intermediate partial sums. However, such wide accumulators increase memory bandwidth usage and reduce energy efficiency. We show that iterative N:M pruning in floating point followed by quantization to 8 (or fewer) bits, and accumulation of partial products in a sorted order ("small to large") allows for accurate, compressed models with short dot product lengths that do not require wide accumulators. We design, analyze, and implement the PQS algorithm to eliminate accumulation overflows at inference time for several neural networks. Our method offers a 2.5x reduction in accumulator bitwidth while achieving model accuracy on par with floating-point baselines for multiple image classification tasks.
Substitutability-Based Graph Node Pricing
Venue: arXiv cs.DB 2025
Year: 2025

In the era o fdat commodification,the pricing o fgraph data presents unique challenges that differ significantly from traditional data markets. This paper addresses the critical issue of node pricing within graph structures, an area that has been largely overlooked in existing literature. We introduce a novel pricing mechanism based on the concept of substitutability, inspired by economic principles, to better reflect the ntrinsic value of nodes in a graph. Unlike previous studies that assumed known prices for nodes or subgraphs, our approach emphasizes the structural significance of nodes by employing a dominator tree, utilizing the Lengauer-Tarjan algorithm to extract dominance relationships. This innovative framework allows us to derive a more realistic pricing strategy that accounts for the unique connectivity and roles of nodes within their respective networks. Our comparative experiments demonstrate that the proposed method significantly outperforms existing pricing strategies, yielding high-quality solutions across various datasets. This research aims to contribute to the existing literature by addressing an important gap and providing insights that may assist in the more effective valuation of graph data, potentially supporting improved decision-making in data-driven environments.
UniFlowRestore: A General Video Restoration Framework via Flow Matching and Prompt Guidance
Venue: arXiv cs.CV 2025
Year: 2025

Video imaging is often affected by complex degradations such as blur, noise, and compression artifacts. Traditional restoration methods follow a "single-task single-model" paradigm, resulting in poor generalization and high computational cost, limiting their applicability in real-world scenarios with diverse degradation types. We propose UniFlowRestore, a general video restoration framework that models restoration as a time-continuous evolution under a prompt-guided and physics-informed vector field. A physics-aware backbone PhysicsUNet encodes degradation priors as potential energy, while PromptGenerator produces task-relevant prompts as momentum. These components define a Hamiltonian system whose vector field integrates inertial dynamics, decaying physical gradients, and prompt-based guidance. The system is optimized via a fixed-step ODE solver to achieve efficient and unified restoration across tasks. Experiments show that UniFlowRestore delivers stateof-the-art performance with strong generalization and efficiency. Quantitative results demonstrate that UniFlowRestore achieves state-of-the-art performance, attaining the highest PSNR (33.89 dB) and SSIM (0.97) on the video denoising task, while maintaining top or second-best scores across all evaluated tasks.
MGS: Markov Greedy Sums for Accurate Low-Bitwidth Floating-Point Accumulation
Venue: arXiv cs.AR 2025
Year: 2025

We offer a novel approach, MGS (Markov Greedy Sums), to improve the accuracy of low-bitwidth floating-point dot products in neural network computations. In conventional 32-bit floating-point summation, adding values with different exponents may lead to loss of precision in the mantissa of the smaller term, which is right-shifted to align with the larger term's exponent. Such shifting (a.k.a. 'swamping') is a significant source of numerical errors in accumulation when implementing low-bitwidth dot products (e.g., 8-bit floating point) as the mantissa has a small number of bits. We avoid most swamping errors by arranging the terms in dot product summation based on their exponents and summing the mantissas without overflowing the low-bitwidth accumulator. We design, analyze, and implement the algorithm to minimize 8-bit floating point error at inference time for several neural networks. In contrast to traditional sequential summation, our method has significantly lowered numerical errors, achieving classification accuracy on par with high-precision floating-point baselines for multiple image classification tasks. Our dMAC hardware units can reduce power consumption by up to 34.1\% relative to conventional MAC units.
A Short Survey on Small Reasoning Models: Training, Inference, Applications and Research Directions
Venue: arXiv cs.AI 2025
Year: 2025

Recently, the reasoning capabilities of large reasoning models (LRMs), such as DeepSeek-R1, have seen significant advancements through the slow thinking process. Despite these achievements, the substantial computational demands of LRMs present considerable challenges. In contrast, small reasoning models (SRMs), often distilled from larger ones, offer greater efficiency and can exhibit distinct capabilities and cognitive trajectories compared to LRMs. This work surveys around 170 recently published papers on SRMs for tackling various complex reasoning tasks. We review the current landscape of SRMs and analyze diverse training and inference techniques related to SRMs. Furthermore, we provide a comprehensive review of SRMs for domain-specific applications and discuss possible future research directions. This survey serves as an essential reference for researchers to leverage or develop SRMs for advanced reasoning functionalities with high efficiency.
Multi-modal and Multi-view Fundus Image Fusion for Retinopathy Diagnosis via Multi-scale Cross-attention and Shifted Window Self-attention
Venue: arXiv cs.CV 2025
Year: 2025

The joint interpretation of multi-modal and multi-view fundus images is critical for retinopathy prevention, as different views can show the complete 3D eyeball field and different modalities can provide complementary lesion areas. Compared with single images, the sequence relationships in multi-modal and multi-view fundus images contain long-range dependencies in lesion features. By modeling the long-range dependencies in these sequences, lesion areas can be more comprehensively mined, and modality-specific lesions can be detected. To learn the long-range dependency relationship and fuse complementary multi-scale lesion features between different fundus modalities, we design a multi-modal fundus image fusion method based on multi-scale cross-attention, which solves the static receptive field problem in previous multi-modal medical fusion methods based on attention. To capture multi-view relative positional relationships between different views and fuse comprehensive lesion features between different views, we design a multi-view fundus image fusion method based on shifted window self-attention, which also solves the computational complexity of the multi-view fundus fusion method based on self-attention is quadratic to the size and number of multi-view fundus images. Finally, we design a multi-task retinopathy diagnosis framework to help ophthalmologists reduce workload and improve diagnostic accuracy by combining the proposed two fusion methods. The experimental results of retinopathy classification and report generation tasks indicate our method's potential to improve the efficiency and reliability of retinopathy diagnosis in clinical practice, achieving a classification accuracy of 82.53\% and a report generation BlEU-1 of 0.543.
Shrinkage Initialization for Smooth Learning of Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

The successes of intelligent systems have quite relied on the artificial learning of information, which lead to the broad applications of neural learning solutions. As a common sense, the training of neural networks can be largely improved by specifically defined initialization, neuron layers as well as the activation functions. Though there are sequential layer based initialization available, the generalized solution to initial stages is still desired. In this work, an improved approach to initialization of neural learning is presented, which adopts the shrinkage approach to initialize the transformation of each layer of networks. It can be universally adapted for the structures of any networks with random layers, while stable performance can be attained. Furthermore, the smooth learning of networks is adopted in this work, due to the diverse influence on neural learning. Experimental results on several artificial data sets demonstrate that, the proposed method is able to present robust results with the shrinkage initialization, and competent for smooth learning of neural networks.
Understanding Intention to Adopt Smart Thermostats: The Role of Individual Predictors and Social Beliefs Across Five EU Countries
Venue: arXiv cs.CY 2025
Year: 2025

Heating of buildings represents a significant share of the energy consumption in Europe. Smart thermostats that capitalize on the data-driven analysis of heating patterns in order to optimize heat supply are a very promising part of building energy management technology. However, factors driving their acceptance by building inhabitants are poorly understood although being a prerequisite for fully tapping on their potential. In order to understand the driving forces of technology adoption in this use case, a large survey (N = 2250) was conducted in five EU countries (Austria, Belgium, Estonia, Germany, Greece). For the data analysis structural equation modelling based on the Unified Theory of Acceptance and Use of Technology (UTAUT) was employed, which was extended by adding social beliefs, including descriptive social norms, collective efficacy, social identity and trust. As a result, performance expectancy, price value, and effort expectancy proved to be the most important predictors overall, with variations across countries. In sum, the adoption of smart thermostats appears more strongly associated with individual beliefs about their functioning, potentially reducing their adoption. At the end of the paper, implications for policy making and marketing of smart heating technologies are discussed.
MASH: Masked Anchored SpHerical Distances for 3D Shape Representation and Generation
Venue: arXiv cs.CV 2025
Year: 2025

We introduce Masked Anchored SpHerical Distances (MASH), a novel multi-view and parametrized representation of 3D shapes. Inspired by multi-view geometry and motivated by the importance of perceptual shape understanding for learning 3D shapes, MASH represents a 3D shape as a collection of observable local surface patches, each defined by a spherical distance function emanating from an anchor point. We further leverage the compactness of spherical harmonics to encode the MASH functions, combined with a generalized view cone with a parameterized base that masks the spatial extent of the spherical function to attain locality. We develop a differentiable optimization algorithm capable of converting any point cloud into a MASH representation accurately approximating ground-truth surfaces with arbitrary geometry and topology. Extensive experiments demonstrate that MASH is versatile for multiple applications including surface reconstruction, shape generation, completion, and blending, achieving superior performance thanks to its unique representation encompassing both implicit and explicit features.
MatWheel: Addressing Data Scarcity in Materials Science Through Synthetic Data
Venue: arXiv cs.LG 2025
Year: 2025

Data scarcity and the high cost of annotation have long been persistent challenges in the field of materials science. Inspired by its potential in other fields like computer vision, we propose the MatWheel framework, which train the material property prediction model using the synthetic data generated by the conditional generative model. We explore two scenarios: fully-supervised and semi-supervised learning. Using CGCNN for property prediction and Con-CDVAE as the conditional generative model, experiments on two data-scarce material property datasets from Matminer database are conducted. Results show that synthetic data has potential in extreme data-scarce scenarios, achieving performance close to or exceeding that of real samples in all two tasks. We also find that pseudo-labels have little impact on generated data quality. Future work will integrate advanced models and optimize generation conditions to boost the effectiveness of the materials data flywheel.
Deconfounded Reasoning for Multimodal Fake News Detection via Causal Intervention
Venue: arXiv cs.MM 2025
Year: 2025

The rapid growth of social media has led to the widespread dissemination of fake news across multiple content forms, including text, images, audio, and video. Traditional unimodal detection methods fall short in addressing complex cross-modal manipulations; as a result, multimodal fake news detection has emerged as a more effective solution. However, existing multimodal approaches, especially in the context of fake news detection on social media, often overlook the confounders hidden within complex cross-modal interactions, leading models to rely on spurious statistical correlations rather than genuine causal mechanisms. In this paper, we propose the Causal Intervention-based Multimodal Deconfounded Detection (CIMDD) framework, which systematically models three types of confounders via a unified Structural Causal Model (SCM): (1) Lexical Semantic Confounder (LSC); (2) Latent Visual Confounder (LVC); (3) Dynamic Cross-Modal Coupling Confounder (DCCC). To mitigate the influence of these confounders, we specifically design three causal modules based on backdoor adjustment, frontdoor adjustment, and cross-modal joint intervention to block spurious correlations from different perspectives and achieve causal disentanglement of representations for deconfounded reasoning. Experimental results on the FakeSV and FVC datasets demonstrate that CIMDD significantly improves detection accuracy, outperforming state-of-the-art methods by 4.27% and 4.80%, respectively. Furthermore, extensive experimental results indicate that CIMDD exhibits strong generalization and robustness across diverse multimodal scenarios.
SBFT Tool Competition 2025 -- Java Test Case Generation Track
Venue: arXiv cs.SE 2025
Year: 2025

This short report presents the 2025 edition of the Java Unit Testing Competition in which four test generation tools (EVOFUZZ, EVOSUITE, BBC, and RANDOOP) were benchmarked on a freshly selected set of 55 Java classes from six different open source projects. The benchmarking was based on structural metrics, such as code and mutation coverage of the classes under test, as well as on the readability of the generated test cases.
Repetitive Contrastive Learning Enhances Mamba's Selectivity in Time Series Prediction
Venue: arXiv cs.LG 2025
Year: 2025

Long sequence prediction is a key challenge in time series forecasting. While Mamba-based models have shown strong performance due to their sequence selection capabilities, they still struggle with insufficient focus on critical time steps and incomplete noise suppression, caused by limited selective abilities. To address this, we introduce Repetitive Contrastive Learning (RCL), a token-level contrastive pretraining framework aimed at enhancing Mamba's selective capabilities. RCL pretrains a single Mamba block to strengthen its selective abilities and then transfers these pretrained parameters to initialize Mamba blocks in various backbone models, improving their temporal prediction performance. RCL uses sequence augmentation with Gaussian noise and applies inter-sequence and intra-sequence contrastive learning to help the Mamba module prioritize information-rich time steps while ignoring noisy ones. Extensive experiments show that RCL consistently boosts the performance of backbone models, surpassing existing methods and achieving state-of-the-art results. Additionally, we propose two metrics to quantify Mamba's selective capabilities, providing theoretical, qualitative, and quantitative evidence for the improvements brought by RCL.
ReferGPT: Towards Zero-Shot Referring Multi-Object Tracking
Venue: arXiv cs.CV 2025
Year: 2025

Tracking multiple objects based on textual queries is a challenging task that requires linking language understanding with object association across frames. Previous works typically train the whole process end-to-end or integrate an additional referring text module into a multi-object tracker, but they both require supervised training and potentially struggle with generalization to open-set queries. In this work, we introduce ReferGPT, a novel zero-shot referring multi-object tracking framework. We provide a multi-modal large language model (MLLM) with spatial knowledge enabling it to generate 3D-aware captions. This enhances its descriptive capabilities and supports a more flexible referring vocabulary without training. We also propose a robust query-matching strategy, leveraging CLIP-based semantic encoding and fuzzy matching to associate MLLM generated captions with user queries. Extensive experiments on Refer-KITTI, Refer-KITTIv2 and Refer-KITTI+ demonstrate that ReferGPT achieves competitive performance against trained methods, showcasing its robustness and zero-shot capabilities in autonomous driving. The codes are available on https://github.com/Tzoulio/ReferGPT
FairACE: Achieving Degree Fairness in Graph Neural Networks via Contrastive and Adversarial Group-Balanced Training
Venue: arXiv cs.LG 2025
Year: 2025

Fairness has been a significant challenge in graph neural networks (GNNs) since degree biases often result in un-equal prediction performance among nodes with varying degrees. Existing GNN models focus on prediction accuracy, frequently overlooking fairness across different degree groups. To addressthis issue, we propose a novel GNN framework, namely Fairness- Aware Asymmetric Contrastive Ensemble (FairACE), which inte-grates asymmetric contrastive learning with adversarial training to improve degree fairness. FairACE captures one-hop local neighborhood information and two-hop monophily similarity to create fairer node representations and employs a degree fairness regulator to balance performance between high-degree and low-degree nodes. During model training, a novel group-balanced fairness loss is proposed to minimize classification disparities across degree groups. In addition, we also propose a novel fairness metric, the Accuracy Distribution Gap (ADG), which can quantitatively assess and ensure equitable performance across different degree-based node groups. Experimental results on both synthetic and real-world datasets demonstrate that FairACE significantly improves degree fairness metrics while maintaining competitive accuracy in comparison to the state-of-the-art GNN models.